class_docstr: Captum Interpretation for Resnet
class_docstr_tok: ["Captum","Interpretation","for","Resnet"]
code: def _get_enc_dec_data(self, inp_data):
      dec_data = self.dls.after_item(inp_data)
      enc_data = self.dls.after_batch(
          to_device(self.dls.before_batch(dec_data), self.dls.device)
      )
      return (enc_data, dec_data)
  
file_path: fastai/fastai/callback/captum.py
imports: from __future__ import annotations
  import tempfile
  from ..basics import *
  from ipykernel import jsonutil
  from captum.attr import IntegratedGradients, NoiseTunnel, GradientShap, Occlusion
  from captum.attr import visualization
  from matplotlib.colors import LinearSegmentedColormap
  from captum.insights import AttributionVisualizer, Batch
  from captum.insights.attr_vis.features import ImageFeature
label_desc: Use image node_id 5 for calling the CaptumInterpretation obj's underlying member method code with example usage: obj._get_enc_dec_data(inp_data) and returns: enc_data,  dec_data